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A Multimodal Robotic Blackjack Dealer: Design, Implementation, and Reliability Analysis
Abstract
We describe a fully integrated blackjack dealing robot system utilizing multimodal input to interact with human players. It can deal cards to players and visually detect which cards have been played. Furthermore, it detects gestures commonly used in blackjack, such as knocking and swiping performed by human players to indicate whether they would like to receive another card. Both, visual and auditory input of the players is processed to achieve robust detection. We demonstrate robust and unobtrusive gameplay. A video showing the system interacting with two players can be seen at https://youtu.be/0XWj5tyZmnY.
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This study investigates the effectiveness of a humanoid service robot (HSR) versus a tablet service kiosk (TSK) along the stages of the point-of-sale (POS) conversion funnel. The observational data gathered by means of a field experiment show that the HSR elicited 26 times more interactions (i.e., passersby touching the screen) than the TSK and that these interactions lasted almost +50% as long. Moreover, more people looked at the store and consequently entered it when the HSR was deployed. Furthermore, more unique transactions were registered, and a higher amount was spent during the days when the HSR was present in front of the store. This study proves that implementing an HSR in the store environment is more effective than a TSK in attracting passersby and converting them into buyers.
Domestic robots such as vacuum cleaners or lawnmowers are becoming popular consumer products in private homes, but while current HCI research on domestic robots has highlighted for example personalisation, long-term effects, or design guidelines, little attention has been paid to automation. To address this, we conducted a qualitative study with 24 participants in private households using interviews, contextual technology tours, and robot deployment. Through thematic analysis we identified three themes related to 1) work routines and automation, 2) domestic robot automation and the physical environment, as well as 3) interaction and breakdown intervention. We present an empirical understanding of how task automation using domestic robots can be implemented in the home. Lastly, we discuss our findings in relation to existing literature and highlight three opportunities for improved task automation using domestic robots for future research.
We present the Dynamic Stack Decider (DSD), a lightweight open-source control architecture. It combines different well-known approaches and is inspired by behavior trees as well as hierarchical state machines . The DSD allows to design and structure complex behavior of robots as well as software agents while providing easy maintainability. Challenges that often occur in robotics, i.e., a dynamic environment and situation uncertainty, remain well-manageable. Furthermore, it allows fast modifications of the control flow, while providing the state-fullness of a state machine. The approach allows developing software using a simple Domain Specific Language (DSL) which defines the control flow and two types of elements that contain the programmed parts. The framework takes care of executing the demanded portions of the code and gives, due to its stack-like internal representation, the ability to verify preconditions while maintaining a clear structure. The presented software was used in different robotic scenarios and showed great performance in terms of flexibility and structuredness.
Background:
The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home.
Methods:
Building on related literature as well as guidance from experts in game design, rehabilitation, and physical and occupational therapy, we developed eight human-robot exercise games for the Baxter Research Robot, six of which involve physical human-robot contact. After extensive iteration, these games were tested in an exploratory user study including 20 younger adult and 20 older adult users.
Results:
Only socially and physically interactive games fell in the highest ranges for pleasantness, enjoyment, engagement, cognitive challenge, and energy level. Our games successfully spanned three different physical, cognitive, and temporal challenge levels. User trust and confidence in Baxter increased significantly between pre- and post-study assessments. Older adults experienced higher exercise, energy, and engagement levels than younger adults, and women rated the robot more highly than men on several survey questions.
Conclusions:
The results indicate that social-physical exercise with a robot is more pleasant, enjoyable, engaging, cognitively challenging, and energetic than similar interactions that lack physical touch. In addition to this main finding, researchers working in similar areas can build on our design practices, our open-source resources, and the age-group and gender differences that we found.
This paper describes a social robotic game player that is able to successfully play a team card game called Sueca. The question we will address in this paper is: how can we build a social robot player that is able to balance its ability to play the card game with natural and social behaviours towards its partner and its opponents. The first challenge we faced concerned the development of a competent artificial player for a hidden information game, whose time constraint is the average human decision time. To accomplish this requirement, the Perfect Information Monte-Carlo (PIMC) algorithm was used. Further, we have performed an analysis of this algorithm's possible parametrizations for games trees that cannot be fully explored in a reasonable amount of time with a MinMax search. Additionally , given the nature of the Sueca game, such robotic player must master the social interactions both as a partner and as an opponent. To do that, an emotional agent framework (FAtiMA) was used to build the emotional and social behaviours of the robot. At each moment, the robot not only plays competitively but also appraises the situation and responds emotionally in a natural manner. To test the approach, we conducted a user study and compared the levels of trust participants attributed to the robots and to human partners. Results have shown that the robot team exhibited a winning rate of 60%. Concerning the social aspects, the results also showed that human players increased their trust in the robot as their game partners (similar to the way to the trust levels change towards human partners). As interactive entertainment expands, computer games are progressively moving from the virtual world back to the physical world. Augmented reality games, haptic interfaces in gaming, touch tables, etc, are some of the types of in-teractivity placing human players in physically situated entertainment experiences. In parallel with this move into the physical world, artificial partners and opponents can also be created to exist in such physical world. To do that, the area of entertainment robots offers challenging opportunities as it explores the role of a robot as a game player. In general, social robots can contribute with new and broad ways of creating socially engaging interactions with humans in entertainment contexts. The challenges of these human-robot interactions may vary from game to game. Some games, when played in the physical world not only hold complex social Copyright c 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. behaviours, but they also hinder performance aspects related with the competitive nature of the game itself. Furthermore, in many types of games, current advances in Artificial Intelligence (AI) over the past few years has shown that strong and powerful algorithms combined with significant amounts of data, are able to defeat human world champions of these games (see for example, the game of Go). These results raise our expectations and people are starting to consider such artificial agents as fierce competitors. Yet, when we consider multi-player games, where the social environment becomes more relevant, and when the games are played in the physical world, how will people perceive a social robotic player compared to human standards? Will people be willing to trust a social robot to be his partner in a team game? To address these questions we created a social autonomous robotic partner for the Sueca card game, with a twofold goal of both playing competitively and interacting socially with the other players. The development of such robotic game player introduces some challenging aspects, in particular finding the best balance between social responses and computations related with the game, in order for the socially intelligent agent to produce natural and human-like behaviours. Another important challenge of creating an intelligent agent in this social context is the time constraint on the computation of a hidden information card game. State-of-the-art approaches, for instance PIMC, promise good results on the Sueca domain according to the game properties. However, the full computation of multiple perfect information games is not time-efficient and will hinder natural interaction in a game with human players. Therefore, this paper also explores how the algorithm's parametrizations affect the game results in order to choose the best performance-time configuration. Finally, by using an expressive robot that is able to express emotions, provide spoken feedback, and respond socially , the game experience can be created balancing these social and game competencies. In this case, we built the social competencies by using an emotional agent framework (FatiMA) which allows for emotional appraisal to occur and fire social and emotional behaviours. At each moment, the robot not only plays competitively, but also appraises the situation and responds emotionally to the game situations. 23 To evaluate such robotic game player, we have considered two central aspects for assessing when playing in the card game: (1) competitiveness between team opponents and (2) cooperation between team partners. Performance can be measured by the number of games won, and simulations made with the developed algorithm to test it against other artificial players. But, most importantly, to assess how humans perceive the robot as a game player we conducted a user study with 60 participants and measured the human players' trust levels in their partners (before and after playing). Concerning the competence of the robot, the results show that the robot is able to play competitively with human players, achieving a winning rate of 60%. We also compared the trust level on the robotic partner with the trust level on human partners. The results show that human players significantly increased their trust in the robot as their game partners (in a similar way to the trust levels they perceive to have towards human partners).
Developing robot agnostic software frameworks involves synthesizing the
disparate fields of robotic theory and software engineering while
simultaneously accounting for a large variability in hardware designs and
control paradigms. As the capabilities of robotic software frameworks increase,
the setup difficulty and learning curve for new users also increase. If the
entry barriers for configuring and using the software on robots is too high,
even the most powerful of frameworks are useless. A growing need exists in
robotic software engineering to aid users in getting started with, and
customizing, the software framework as necessary for particular robotic
applications. In this paper a case study is presented for the best practices
found for lowering the barrier of entry in the MoveIt! framework, an
open-source tool for mobile manipulation in ROS, that allows users to 1)
quickly get basic motion planning functionality with minimal initial setup, 2)
automate its configuration and optimization, and 3) easily customize its
components. A graphical interface that assists the user in configuring MoveIt!
is the cornerstone of our approach, coupled with the use of an existing
standardized robot model for input, automatically generated robot-specific
configuration files, and a plugin-based architecture for extensibility. These
best practices are summarized into a set of barrier to entry design principles
applicable to other robotic software. The approaches for lowering the entry
barrier are evaluated by usage statistics, a user survey, and compared against
our design objectives for their effectiveness to users.
The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).
We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
Based on a study of the engagement process between humans, we have developed and implemented an initial computational model for recognizing engagement between a human and a humanoid robot. Our model contains recognizers for four types of connection events involving gesture and speech: directed gaze, mutual facial gaze, conversational adjacency pairs and backchannels. To facilitate integrating and experimenting with our model in a broad range of robot architectures, we have packaged it as a node in the open-source Robot Operating System (ROS) framework. We have conducted a preliminary validation of our computational model and implementation in a simple human-robot pointing game.
A simple and efficient randomized algorithm is presented for
solving single-query path planning problems in high-dimensional
configuration spaces. The method works by incrementally building two
rapidly-exploring random trees (RRTs) rooted at the start and the goal
configurations. The trees each explore space around them and also
advance towards each other through, the use of a simple greedy
heuristic. Although originally designed to plan motions for a human arm
(modeled as a 7-DOF kinematic chain) for the automatic graphic animation
of collision-free grasping and manipulation tasks, the algorithm has
been successfully applied to a variety of path planning problems.
Computed examples include generating collision-free motions for rigid
objects in 2D and 3D, and collision-free manipulation motions for a
6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is
also presented
This paper describes a social robotic game player that is able to successfully play a team card game called Sueca. The question we will address in this paper is: how can we build a social robot player that is able to balance its ability to play the card game with natural and social behaviours towards its partner and its opponents. The first challenge we faced concerned the development of a competent artificial player for a hidden information game, whose time constraint is the average human decision time. To accomplish this requirement, the Perfect Information Monte Carlo (PIMC) algorithm was used. Further, we have performed an analysis of this algorithm's possible parametrizations for games trees that cannot be fully explored in a reasonable amount of time with a MinMax search. Additionally, given the nature of the Sueca game, such robotic player must master the social interactions both as a partner and as an opponent. To do that, an emotional agent framework (FAtiMA) was used to build the emotional and social behaviours of the robot. At each moment, the robot not only plays competitively but also appraises the situation and responds emotionally in a natural manner. To test the approach, we conducted a user study and compared the levels of trust participants attributed to the robots and to human partners. Results have shown that the robot team exhibited a winning rate of 60%. Concerning the social aspects, the results also showed that human players increased their trust in the robot as their game partners (similar to the way to the trust levels change towards human partners).
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at https://pjreddie.com/yolo/
Robots executing practical tasks in real environments are often subject to multiple constraints. These constraints include orientation constraints: e.g., keeping a glass of water upright, torque constraints: e.g., not exceeding the torque limits for an arm lifting heavy objects, visibility constraints: e.g., keeping an object in view while moving a robot arm, etc. Rejection sampling, Jacobian projection techniques and optimization-based approaches are just some of the methods that have been used to address such constraints while computing motion plans for robots performing manipulation tasks. In this work, we present an approach to handling certain types of constraints in a manner that significantly increases the efficiency of existing methods. Our approach focuses on the sampling step of a motion planner. We implement this step as the drawing of samples from a set that has been computed in advance instead of the direct sampling of constraints. We show how our approach can be applied to different constraints: orientation constraints on the end-effector of an arm, visibility constraints and dual-arm constraints. We present simulated results to validate our method, comparing it to approaches that use direct sampling of constraints.
This article discusses the card game blackjack as played in the casinos of Las Vegas. The basic rules for the game are described in detail. The player's strategic problems are analyzed with the objective of finding the strategy maximizing his mathematical expectation.A mathematical expression is derived giving a general solution to the player's problem of standing pat with a given hand versus drawing additional cards. No general solutions are possible for the other major strategic problems, however, and a detailed examination of individual situations is required. The formulas and methods for the case analysis are stated, but computational details are omitted. Similarly, the formula for the player's mathematical expectation is stated, but its numerical evaluation is not described. Detailed discussion is given to the problems arising in the combinatorial type of computations required by blackjack.The “optimum strategy” determined by the above analysis differs substantially from the published strategies of card experts and the usual style of play in the casinos.
Abstract— This paper gives an overview of ROS, an open- source robot operating,system. ROS is not an operating,system in the traditional sense of process management,and scheduling; rather, it provides a structured communications layer above the host operating,systems,of a heterogenous,compute,cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software,which,uses ROS.
All of the image recognition, the RFID recognition, the robotic control, the robotic kinematics, the robotic motion trajectory generation methods, and a robot are integrated into the Intelligent Interaction Dealer Robot. This dealer robot can imitate professional actions of a real card dealer to deal out poker cards in the front of players, recognize points of every card by image recognition system, count bets by RFID recognition system, pay or take chips by judging players' and the dealer's points, and collect all cards when this gambling have been finished. By using these recognition systems and the robotic action databases, the dealer robot can play a professional card dealer by itself to serve players in casino. The characteristics of the dealer robot include: 1. The dealer robot with two mimetic arms is designed and manufactured by ourselves. 2. Two mimetic machine arms are specially designed as one adsorption right arm and one sweeping left arm. 3. The robotic kinematics and fuzzy approximation are used to generate the robotic motion trajectories to establish the robotic action databases. 4. The image and RFID recognition methods are used to recognize points of cards and count bets of chips. In a word, the dealer service robot is developed to serve players in a casino. By using this dealer robot, the human resource cost can be reduced and the competitiveness of the tourism and gambling industry can be enhanced.
Recognizing engagement in human-robot interaction
- C Rich
- B Ponsler
- A Holroyd
- C L Sidner
- Joseph Redmon
- Ali Farhadi
Joseph Redmon and Ali Farhadi. YOLOv3: An Incremental Improvement. arXiv
preprint arXiv:1804.02767, 2018.
- Fan Zhang
- Valentin Bazarevsky
- Andrey Vakunov
- Andrei Tkachenka
- George Sung
- Chuo-Ling Chang
- Matthias Grundmann
Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George
Sung, Chuo-Ling Chang, and Matthias Grundmann. MediaPipe Hands: On-device
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